SuperNNova: A Python repository from JulienPeloton

Read the documentation

For the main branch: https://supernnova.readthedocs.io

The paper branch differs slightly from the master. Take a look to "changelog_paper_to_new_branch" or Build the docs for this branch.

Installation

Clone this repository (preferred)

git clone https://github.com/supernnova/supernnova.git

or install pip module (check versioning)

pip install supernnova

Read the paper

Links to the publication: MNRAS,ArXiv. All results quoted in these publications were produced using the branch "paper" which is frozen for reproducibility.

Please include the full citation if you use this material in your research: A Möller and T de Boissière, MNRAS, Volume 491, Issue 3, January 2020, Pages 4277–4293.

Repository overview
Getting Started
1. With Conda
2. With Docker
Usage
Reproduce paper
Pipeline Description
Running tests
Build the docs

Repository overview

├── supernnova              --> main module
    ├──data                 --> scripts to create the processed database
    ├──visualization        --> data plotting scripts
    ├──training             --> training scripts
    ├──validation           --> validation scripts
    ├──utils                --> utilities used throughout the module
├── tests                   --> unit tests to check data processing
├── sandbox                 --> WIP scripts

Getting started

With Conda

cd env

# Create conda environment
conda create --name <env> --file <conda_file_of_your_choice>

# Activate conda environment
source activate <env>

With Docker

cd env

# Build docker images
make cpu  # cpu image
make gpu  # gpu image (requires NVIDIA Drivers + nvidia-docker)

# Launch docker container
python launch_docker.py (--use_gpu to run GPU based container)

For more detailed instructions, check the full setup instructions

Usage

When cloning this repository:

# Create data
python run.py --data  --dump_dir tests/dump --raw_dir tests/raw --fits_dir tests/fits

# Train a baseline RNN
python run.py --train_rnn --dump_dir tests/dump

# Train a variational dropout RNN
python run.py --train_rnn --model variational --dump_dir tests/dump

# Train a Bayes By Backprop RNN
python run.py --train_rnn --model bayesian --dump_dir tests/dump

# Train a RandomForest
python run.py --train_rf --dump_dir tests/dump

When using pip, a full example is https://supernnova.readthedocs.io

# Python
import supernnova.conf as conf
from supernnova.data import make_dataset

# get config args
args =  conf.get_args()

# create database
args.data = True            # conf: making new dataset
args.dump_dir = "tests/dump"        # conf: where the dataset will be saved
args.raw_dir = "tests/raw"      # conf: where raw photometry files are saved 
args.fits_dir = "tests/fits"        # conf: where salt2fits are saved 
settings = conf.get_settings(args)  # conf: set settings
make_dataset.make_dataset(settings) # make dataset

Reproduce paper results

Please change to branch paper:

python run_paper.py

General pipeline description

Parse raw data in FITS format
Create processed database in HDF5 format
Train Recurrent Neural Networks (RNN) or Random Forests (RF) to classify photometric lightcurves
Validate on test set

Running tests with py.test

PYTHONPATH=$PWD:$PYTHONPATH pytest -W ignore --cov supernnova tests

Build docs

cd docs && make clean && make html && cd ..
firefox docs/_build/html/index.html

JulienPeloton/SuperNNova